Garbage In, Consensus Out: Part I

In the July 8 Wall Street Journal, Robert J. Caprara describes the process of computer modeling, and the motivations of the modeler. He was a consultant charged with building a detailed computer model of the nation’s fresh water sources, including drinking water intakes and sewage discharges. He tuned and tweaked the model, and was happy with his preliminary conclusion: the EPA program he had been asked to study had reached a point of diminishing returns and should be wound down.

Confessions of a Computer Modeler (Paywall)

Any model, including those predicting climate doom, can be tweaked to yield a desired result. I should know.

When I presented the results to the EPA official in charge, he said that I should go back and “sharpen my pencil.” I did. I reviewed assumptions, tweaked coefficients and recalibrated data. But when I reran everything the numbers didn’t change much. At our next meeting he told me to run the numbers again.

After three iterations I finally blurted out, “What number are you looking for?” He didn’t miss a beat: He told me that he needed to show $2 billion of benefits to get the program renewed. I finally turned enough knobs to get the answer he wanted, and everyone was happy. …

I realized that my work for the EPA wasn’t that of a scientist, at least in the popular imagination of what a scientist does. It was more like that of a lawyer. My job, as a modeler, was to build the best case for my client’s position. The opposition will build its best case for the counter argument and ultimately the truth should prevail. …

Surely the scientific community wouldn’t succumb to these pressures like us money-grabbing consultants. Aren’t they laboring for knowledge instead of profit? If you believe that, boy do I have a computer model to sell you.

A terrific op-ed; you should read it all if possible.

Image from International Reservoir Technologies, Inc.

Modeling Oil and Gas Reservoirs

Reservoir modeling per se is not my area of expertise. In my capacity as a technical manager for an oil company, models have been prepared by others under my supervision. Basically, I didn’t run the software, but I needed enough of a working knowledge of the process to be able to understand it and ask intelligent questions.

The goal of modeling is to create a numerical representation of a reservoir that honors what is known about the system to be able to make accurate forecasts about its future behavior.

The first step in modeling a reservoir is to represent its geometry, as shown above. Each cell in the representation is represented by descriptive parameters, such as volume, pressure, porosity, fluid transmissibility and fluid saturations. As production is “withdrawn” from the model in each time step, the software honors the physical constraints (gravity, fluid flow laws, conservation of mass, etc.) to calculate the interaction of every cell in the model, how fluids move within the model, and to what extent pressure decreases.

The next step in the modeling process is achieving an acceptable “history match”. Through the production phase, withdrawal of fluids (oil, gas and water) have been carefully recorded over time, along with reservoir pressure. What follows is a tedious process wherein various parameters are tweaked and tuned so that the model’s performance is the same as actual observation. Only when close agreement has been achieved between field performance and the model can the modeler proceed to the next step: prediction.

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